Cortical parcellation optimized for magnetoencephalography with a clustering technique
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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14
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Scientific Reports, Volume 15, issue 1, pp. 1-14
Abstract
A typical approach to estimate connectivity from magnetoencephalographic (MEG) data consists of 1) computing a cortically-constrained, distributed source estimate, 2) dividing the cortex into parcels according to an anatomical atlas, 3) combining the source time courses within each parcel, and 4) computing a connectivity metric between these combined time courses. However, combining MEG signals to spatial mean activities of anatomically-defined parcels often leads to cancellation within and crosstalk between parcels. We present a method that divides the cortex into parcels whose activity can be faithfully represented by a single dipolar source while minimizing inter-parcel crosstalk. The method relies on unsupervised clustering of the MEG leadfields, also accounting for distances between the cortically-constrained sources to promote spatially contiguous parcels. The cluster each source point belongs to is determined by its k nearest-neighbour memberships. Inter-parcel crosstalk was minimized by assigning and a weight of 20%-40% to the spatial distances, leading to 60–120 parcels. Our approach, available through the Python package “megicparc”, enables a compact yet anatomically-informed source-level representation of MEG data with a similar dimensionality as in the original sensor-level data. Such representation should enable significant improvements in source-space visualization of MEG features or in estimating functional connectivity.Description
Publisher Copyright: © The Author(s) 2025.
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Sommariva, S, Subramaniyam, N P & Parkkonen, L 2025, 'Cortical parcellation optimized for magnetoencephalography with a clustering technique', Scientific Reports, vol. 15, no. 1, 6404, pp. 1-14. https://doi.org/10.1038/s41598-025-90166-1